Retracted: Privacy and Security Enabling for Healthcare Data using Lightweight Deep learning with Cryptography

Abstract

These days, smart devices are essential in dislodging the traditional healthcare management system. In this case, the Internet of Things (IoT) propels the healthcare sector's growth by enabling connections between objects at any time and from any location, even in a diverse setting. However, two main issues with IoT-enabled medical infrastructure are the security of patient data and protecting customer privacy. In order to safeguard healthcare data in IoT-enabled healthcare infrastructure, this study provides a unique encryption strategy using elliptic curve cryptography, Advanced Encryption Standard (AES), and Serpent. By integrating both symmetric and asymmetric-based encryption approaches, the proposed hybrid encryption technique enhances security measures for healthcare data. Additionally, the proposed scheme uses an elliptic curve-based digital signature to guarantee data integrity. This study presents formal security analysis as well as performance comparisons to demonstrate the effectiveness of the proposed approach. Results and analysis demonstrate the success of the presented methodology.